Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran. Department of Electrical Engineering, Persian Gulf University, Bushehr, Iran.
J Neural Eng. 2020 Jun 25;17(3):036031. doi: 10.1088/1741-2552/ab965a.
Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve the accuracy of sleep stage classification in term neonates.
The classification performance was evaluated on quiet sleep (QS) and active sleep (AS) stages, each with two sub-states, using multichannel EEG data recorded from sixteen neonates with postmenstrual age of 38-40 weeks. A comprehensive set of linear and nonlinear features were extracted from thirty-second EEG segments. The feature space dimensionality was then reduced by using an evolutionary feature selection method called MGCACO (Modified Graph Clustering Ant Colony Optimization) based on the relevance and redundancy analysis. A bi-directional long-short time memory (BiLSTM) network was trained for sleep stage classification. The number of channels was optimized using the sequential forward selection method to reduce the spatial space. Finally, an HMM-based postprocessing stage was used to reduce false positives by incorporating the knowledge of transition probabilities between stages into the classification process. The method performance was evaluated using the K-fold (KFCV) and leave-one-out cross-validation (LOOCV) strategies.
Using six-bipolar channels, our method achieved a mean kappa and an overall accuracy of 0.71-0.76 and 78.9%-82.4% using the KFCV and LOOCV strategies, respectively.
The presented automatic sleep stage scoring method can be used to study the neurodevelopmental process and to diagnose brain abnormalities in term neonates.
自动睡眠分期对于研究婴儿期的睡眠结构非常重要。在这项工作中，我们引入了一种新的基于深度学习网络和隐马尔可夫模型（HMM）的多通道方法，以提高足月新生儿睡眠分期分类的准确性。
使用来自 16 名具有 38-40 周胎龄的新生儿的多通道 EEG 数据，评估安静睡眠（QS）和活跃睡眠（AS）阶段的分类性能，每个阶段有两个亚态。从 32 秒 EEG 段中提取了一系列线性和非线性特征。然后使用基于相关性和冗余分析的改进图聚类蚁群优化（MGCACO）的进化特征选择方法来降低特征空间的维度。为了进行睡眠阶段分类，训练了一个双向长短期记忆（BiLSTM）网络。使用顺序前向选择方法优化通道数量，以减少空间空间。最后，使用基于 HMM 的后处理阶段，通过将阶段之间的转移概率知识纳入分类过程来减少假阳性。使用 K 折（KFCV）和留一交叉验证（LOOCV）策略评估方法性能。
使用六极导联，我们的方法在 KFCV 和 LOOCV 策略下分别实现了 0.71-0.76 的平均kappa 和 78.9%-82.4%的整体准确性。
所提出的自动睡眠分期方法可用于研究神经发育过程并诊断足月新生儿的脑异常。
